Multi-dimensional network
status visualization
and client experience
awareness throughout
the journey |
- Allows administrators to view multi-dimensional data statistics based on different levels and regions.
- Allows administrators to view issues about network access, network congestion, device status, and error packets from the perspective of buildings.
- Network users can be searched based on buildings, displaying information about buildings that users passed by in a specified period of time.
- Allows administrators to import topology views and plan AP locations to intuitively view fault location distribution.
- Allows administrators to view the radio heat map by AP location.
- Allows administrators to import network planning data to be compared with the actual network data, displaying the differences between them.
- Displays spectrum analysis results based on APs, including full-channel status monitoring, Wi-Fi interference sources, and non-Wi-Fi interference sources.
- Generates dialing test reports for multi-vendor network comparison in real-time and
allows administrators to intuitively learn the Wi-Fi network experience through dialing tests
on apps.
- Allows administrators to view the full-journey experience, including who, when, and which AP to
connect, experience, and issues.
- Supports device profiles and allows administrators to view the health status of switches and
APs.
- Traces the network access process of a client, including detailed protocol information at the association, authentication (supporting 802.1X, portal, and MAC address), and DHCP phases. The protocol information includes the interaction result and time used. If the interaction fails, the failure causes are also displayed.
- Correlatively analyzes poor-experience network users. When the experience of a user
deteriorates, CampusInsight identifies quantified correlation KPIs based on the KPI
similarity analysis algorithm, which effectively improves the accuracy of root cause
identification.
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Automatic identification
and proactive prediction
of network issues |
- Supports automatic identification of common network issues based on big data
analytics and ML algorithms: connectivity, air interface performance,
roaming, device environment, device capacity, network performance, and network
status issues. The issues include authentication failure, weak-signal coverage, dual
band-capable clients prioritizing 2.4G, and network congestion.
- Supports learning and dynamic baseline drawing on network behavior to predict the
change trend and detect exceptions through data comparison.
- Intelligently analyzes data reported at the second level and establishes a network health evaluation system from multiple dimensions. CampusInsight evaluates and ranks regions based on indicator weights, driving continuous improvement from poor experience to good experience and gradually improving the network quality. The dynamic baseline comparison between the local region and other regions for each indicator can be viewed. CampusInsight provides associated root cause indicators, enabling in-depth root cause analysis. Different time or areas can be selected for comparison and analysis and network health reports are sent to administrators in real-time or periodically by email.
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Intelligent demarcation
and root cause analysis
of network issues |
- Issue distribution view allows administrators to view the number of issues on different devices and the number of affected clients. This helps administrators quickly focus on the affected devices and the time range when many issues occur.
- Issue impact analysis view allows administrators to filter impact factors from multiple dimensions and drill down layer by layer to quickly locate the issue root cause.
- Analyzes the root causes and provides rectification suggestions to assist quick
issue closure.
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Open northbound APIs,
providing various data
for intelligent analysis |
- Supports different secondary development capabilities based on data characteristics.
Three types of interfaces can open the raw data and analyzed data to third-party
systems, including network O&M and IT service systems, thereby offering richer
intelligent analysis data.
(1) RESTful NBI: Opens resource data (device, interface, link, and board data), health
data (health issue and health evaluation data) and terminal session data to external
systems.
(2) SNMP NBI: Reports alarm data to a third-party system through SNMP.
(3) Kafka NBI: Consumes data collected by CampusInsight using telemetry through the
consumer API provided by Kafka.
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